text/microsoft-resx 2.0 System.Resources.ResXResourceReader, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 System.Resources.ResXResourceWriter, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 Contrast Adjustment Adjust image contrast and brightness Contrast Contrast gain, 1.0 for original contrast Brightness Brightness offset Auto Contrast Automatically stretch contrast to full range Use CLAHE Use Contrast Limited Adaptive Histogram Equalization CLAHE Clip Limit CLAHE contrast limit threshold Band Pass Filter Preserve image information within specified frequency range Low Cutoff Radius Components below this frequency will be removed High Cutoff Radius Components above this frequency will be removed Filter Type Transition characteristics of the filter Butterworth Order Order of Butterworth filter Contour Detection Detect contours in image and output contour information Target Color Select the color of regions to find (white or black) Enable Threshold Apply binary threshold before contour detection Threshold Value Threshold value for binarization (0-255) Use Otsu Auto Threshold Automatically calculate optimal threshold Min Area Filter contours smaller than this area Max Area Filter contours larger than this area Line Thickness Thickness of contour lines Division Operation Perform division operation on image for background correction and normalization Divisor Each pixel value will be divided by this number Scale Factor Division result multiplied by this scale factor Normalize Output Normalize result to 0-255 range Gamma Correction Adjust image brightness through Gamma value Gamma Value Gamma value, less than 1 darkens image, greater than 1 brightens image Gain Output gain coefficient Gaussian Blur Smooth image using Gaussian kernel Kernel Size Size of Gaussian kernel, must be odd Standard Deviation Standard deviation of Gaussian kernel, controls blur amount Morphology Processing Perform morphological operations (erosion, dilation, opening, closing) Operation Type Select morphological operation type Kernel Size Size of structuring element Iterations Number of times to repeat morphological operation Shock Filter Edge enhancement and denoising Iterations Number of filter iterations Threshold Edge detection threshold Time Step Evolution time step Threshold Segmentation Binarize image Threshold Binarization threshold, pixels above this value will be set to max value Max Value Pixels above threshold will be set to this value Use Otsu Auto Threshold When enabled, optimal threshold will be calculated automatically Comprehensive Filter Integrated multiple filtering methods Filter Type Select filtering method Kernel Size Size of the filter kernel (must be odd) Sigma Standard deviation for Gaussian/Bilateral filter Cutoff Frequency Cutoff frequency for frequency domain filtering Low Cutoff Radius Components below this frequency will be removed High Cutoff Radius Components above this frequency will be removed Band Pass Filter Type Transition characteristics of the band pass filter Butterworth Order Order of Butterworth filter Gaussian Filter Smooth image and reduce Gaussian noise while preserving edges Median Filter Remove salt-and-pepper noise effectively Mean Filter Simple averaging smoothing filter Bilateral Filter Edge-preserving smoothing filter Low Pass Filter Remove high frequency noise in frequency domain High Pass Filter Edge enhancement in frequency domain Band Pass Filter Preserve image information within specified frequency range Median Filter Remove salt-and-pepper noise effectively Kernel Size Size of the filter kernel (must be odd) Mean Filter Simple averaging smoothing filter Kernel Size Size of the filter kernel (must be odd) Bilateral Filter Edge-preserving smoothing filter Diameter Diameter of each pixel neighborhood Sigma Color Filter sigma in the color space Sigma Space Filter sigma in the coordinate space Low Pass Filter Remove high frequency noise in frequency domain Cutoff Frequency Cutoff frequency for low pass filter High Pass Filter Edge enhancement in frequency domain Cutoff Frequency Cutoff frequency for high pass filter Grayscale Conversion Convert image to grayscale Conversion Method Method for grayscale conversion Sharpen Enhance image edges and details Sharpen Method Select sharpening algorithm Strength Strength of sharpening effect Kernel Size Size of sharpening kernel (must be odd) Histogram Equalization Enhance image contrast Equalization Method Select histogram equalization algorithm Clip Limit Contrast limiting threshold for CLAHE Tile Size Tile size for CLAHE Sobel Edge Detection Detect image edges using Sobel operator Detection Direction Select edge detection direction Kernel Size Sobel operator kernel size (odd number) Scale Factor Scale factor for edge intensity Kirsch Edge Detection Detect image edges using Kirsch operator Threshold Edge detection threshold Scale Factor Scale factor for edge intensity Horizontal Edge Detection Detect horizontal edges specifically Detection Method Select horizontal edge detection algorithm Sensitivity Edge detection sensitivity Threshold Edge detection threshold Retinex Shadow Correction Multi-scale shadow correction and illumination equalization based on Retinex Processing Method Select Retinex algorithm type Scale 1 (Small) Small scale Gaussian kernel sigma for detail enhancement Scale 2 (Medium) Medium scale Gaussian kernel sigma for local illumination correction Scale 3 (Large) Large scale Gaussian kernel sigma for global illumination correction Gain Output gain factor Offset Output offset value HDR Enhancement High Dynamic Range image enhancement with tone mapping Tone Mapping Method Select HDR tone mapping algorithm: LocalToneMap, AdaptiveLog, Drago, BilateralToneMap Gamma Gamma correction value for output brightness adjustment Saturation Contrast saturation factor for LocalToneMap method Detail Boost Detail enhancement factor, higher values reveal more fine details Sigma Space Spatial sigma for base layer extraction, controls smoothing range Sigma Color Color sigma for bilateral tone mapping, controls edge preservation Bias Bias for adaptive logarithmic and Drago mapping, controls dark/bright balance Mirror Flip image horizontally, vertically, or both Direction Flip direction: Horizontal (left-right), Vertical (up-down), Both (180° rotation) Rotate Rotate image by arbitrary angle with optional canvas expansion Angle Rotation angle in degrees, positive is counter-clockwise Expand Canvas Expand canvas to fit the entire rotated image, otherwise crop to original size Background Background fill value (0-255) for areas outside the original image Interpolation Interpolation method: Nearest (fast), Bilinear (smooth), Bicubic (high quality) Pseudo Color Rendering Map grayscale image to color image using color maps Color Map Select color mapping table for rendering Min Gray Value Gray values below this will be clipped to minimum color Max Gray Value Gray values above this will be clipped to maximum color Invert Color Map Reverse the color mapping direction Electronic Film Effect Simulate traditional X-ray film display with window/level and characteristic curves Window Center Center gray value of the display window (Window Level) Window Width Width of the display window, controls visible gray range Invert (Negative) Invert image to negative film effect Characteristic Curve Film characteristic curve: Linear, Sigmoid (S-curve), Logarithmic, Exponential Curve Strength Strength of the characteristic curve effect Edge Enhancement Edge enhancement strength to simulate film sharpening, 0 to disable Sub-Pixel Zoom High-quality sub-pixel image magnification with multiple interpolation methods Scale Factor Magnification factor, supports fractional values (e.g. 1.5, 2.3) Interpolation Interpolation method: Nearest, Bilinear, Bicubic, Lanczos (highest quality) Sharpen After Zoom Apply sharpening after magnification to compensate interpolation blur Sharpen Strength Strength of post-zoom sharpening Super Resolution (AI) Deep learning based super resolution using EDSR/FSRCNN models Model Super resolution model: EDSR (high quality, slow) or FSRCNN (fast, lightweight) Scale Factor Upscaling factor: 2x, 3x, or 4x Color Layer Separation Separate grayscale image into distinct intensity layers Layers Number of intensity layers (2-16) Method Thresholding method: Uniform (equal intervals) or Otsu (adaptive) Output Mode Output mapping: EqualSpaced (evenly distributed) or MidValue (interval midpoint) Target Layer 0 = show all layers, 1~N = show only the specified layer (white) with others black Hierarchical Enhancement Enhance image details at different scales using Laplacian pyramid decomposition Pyramid Levels Number of pyramid decomposition levels (2-8) Fine Detail Gain Gain for finest detail layer (edges, textures). 1.0 = original, >1 = enhance, <1 = suppress Medium Detail Gain Gain for medium-scale details. 1.0 = original, >1 = enhance, <1 = suppress Coarse Detail Gain Gain for coarse-scale details (large structures). 1.0 = original, >1 = enhance, <1 = suppress Base Layer Gain Gain for the base (lowest frequency) layer, controls overall brightness Clip Limit Limit detail amplitude to prevent over-enhancement artifacts. 0 = no limit Histogram Overlay Compute grayscale histogram and overlay it on the top-left corner of the image with statistics Ellipse Detection Detect ellipses in image using contour analysis and ellipse fitting Min Threshold Minimum threshold for dual-threshold segmentation (0-255) Max Threshold Maximum threshold for dual-threshold segmentation (0-255) Use Otsu Auto Threshold When enabled, optimal threshold will be calculated automatically Min Contour Points Minimum number of contour points for ellipse fitting Min Area Filter ellipses smaller than this area Max Area Filter ellipses larger than this area Max Eccentricity Maximum eccentricity (0=circle, close to 1=flat ellipse) Max Fit Error Maximum fitting error in pixels Line Thickness Thickness of ellipse drawing lines Line Measurement Measure distance between two points in the image Point 1 X X coordinate of the first point (pixels) Point 1 Y Y coordinate of the first point (pixels) Point 2 X X coordinate of the second point (pixels) Point 2 Y Y coordinate of the second point (pixels) Pixel Size Physical size per pixel for calibrated measurement Unit Measurement unit: px (pixels), mm, μm, cm Line Thickness Thickness of the measurement line Show Label Display distance label on the measurement line Via Fill Rate (Tilted Geometric) Measure via fill rate using 4-ellipse geometric projection on tilted X-Ray image THT Limit (%) Minimum fill rate to pass (default 75% per IPC-610) Line Thickness Thickness of ROI ellipse lines BGA Void Rate (Auto) Auto-detect BGA solder balls and measure void rate (two-step: locate BGA → detect voids) BGA Min Area Minimum pixel area to identify as a BGA solder ball ROI Mode None: full image; Polygon: polygon ROI (draw ROI first) BGA Max Area Maximum pixel area to identify as a BGA solder ball Blur Kernel Gaussian blur kernel size for BGA detection (odd number) Min Circularity Minimum circularity to filter non-circular contours (0~1, 1=perfect circle) Void Limit (%) Maximum allowed void rate (default 25% per IPC-7095) Min Threshold Minimum gray value for void detection (pixels in [Min,Max] = void) Max Threshold Maximum gray value for void detection Min Void Area Minimum pixel area to count as a void (filter noise) Line Thickness Thickness of contour lines Point-to-Line Distance Measure perpendicular distance from a point to a line Pixel Size Physical size per pixel for calibrated measurement Unit Measurement unit: px / mm / μm / cm Line Thickness Thickness of drawing lines Angle Measurement Measure angle between two rays sharing a common vertex Void Measurement Detect voids in ROI and calculate void rate Min Threshold Lower grayscale bound for void detection Max Threshold Upper grayscale bound for void detection Min Void Area Voids smaller than this area are ignored (pixels) Merge Radius Dilation radius for merging adjacent voids (0=no merge) Blur Size Gaussian blur kernel size (odd number) Void Limit (%) Void rate above this limit is classified as FAIL Emboss Pseudo-3D Emboss effect simulating 3D relief for enhanced visualization of surface structures Light Direction Simulated light source direction for emboss effect Emboss Depth Depth of emboss effect (higher = stronger relief) Original Blend Blend ratio with original image (0=pure emboss, 1=original only) Gray Offset Gray level offset for flat areas (128=mid-gray base)