fix: 修复算子代码审查发现的关键问题

1. 资源泄漏：HighPass/LowPass滤波器Image对象未dispose(改用using+Clone)
2. 线程安全：GammaProcessor LUT从实例字段改为方法局部变量
3. 资源泄漏：BandPassFilter补充floatImage和mask的Dispose
4. 边界情况：ThresholdProcessor Otsu16初始阈值改为中值(防全黑/全白)
5. 异常安全：RemoveOutliers的medianImage用try-finally保证释放
6. 空引用：SuperResolution InputMetadata空检查

XP.ImageProcessing.Processors/图像变换/ThresholdProcessor.cs

@@ -128,6 +128,8 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
                 histogram[data[y, x, 0]]++;
 
         long totalPixels = (long)w * h;
+        if (totalPixels == 0) return maxVal / 2;
+
         double totalSum = 0;
         for (int i = 0; i < levels; i++)
             totalSum += (double)i * histogram[i];
@@ -135,7 +137,7 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
         double bgSum = 0;
         long bgPixels = 0;
         double maxVariance = -1;
-        int bestThreshold = 0;
+        int bestThreshold = maxVal / 2; // Default to midpoint if no valid threshold found
 
         for (int t = 0; t < levels; t++)
         {

XP.ImageProcessing.Processors/图像增强/GammaProcessor.cs

@@ -25,8 +25,6 @@ namespace XP.ImageProcessing.Processors;
 public class GammaProcessor<TDepth> : ImageProcessorBase<TDepth>
     where TDepth : struct, IComparable
 {
-    private byte[] _lookupTable8 = new byte[256];
-    private ushort[] _lookupTable16 = new ushort[65536];
     private static readonly ILogger _logger = Log.ForContext<GammaProcessor<TDepth>>();
 
     public GammaProcessor()
@@ -64,49 +62,53 @@ public class GammaProcessor<TDepth> : ImageProcessorBase<TDepth>
 
         if (typeof(TDepth) == typeof(ushort))
         {
-            BuildLUT16(gamma, gain);
+            var lut16 = BuildLUT16(gamma, gain);
             var img16 = inputImage as Image<Gray, ushort>;
             var result = new Image<Gray, ushort>(inputImage.Width, inputImage.Height);
             Parallel.For(0, inputImage.Height, y =>
             {
                 for (int x = 0; x < inputImage.Width; x++)
-                    result.Data[y, x, 0] = _lookupTable16[img16!.Data[y, x, 0]];
+                    result.Data[y, x, 0] = lut16[img16!.Data[y, x, 0]];
             });
             _logger.Debug("Process(16bit): Gamma={G}, Gain={Gain}", gamma, gain);
             return (result as Image<Gray, TDepth>)!;
         }
         else
         {
-            BuildLUT8(gamma, gain);
+            var lut8 = BuildLUT8(gamma, gain);
             var result = (inputImage as Image<Gray, byte>)!.Clone();
             int h = inputImage.Height, w = inputImage.Width;
             for (int y = 0; y < h; y++)
                 for (int x = 0; x < w; x++)
-                    result.Data[y, x, 0] = _lookupTable8[result.Data[y, x, 0]];
+                    result.Data[y, x, 0] = lut8[result.Data[y, x, 0]];
             _logger.Debug("Process(8bit): Gamma={G}, Gain={Gain}", gamma, gain);
             return (result as Image<Gray, TDepth>)!;
         }
     }
 
-    private void BuildLUT8(double gamma, double gain)
+    private static byte[] BuildLUT8(double gamma, double gain)
     {
+        var lut = new byte[256];
         double invGamma = 1.0 / gamma;
         for (int i = 0; i < 256; i++)
         {
             double normalized = i / 255.0;
             double corrected = Math.Pow(normalized, invGamma) * gain;
-            _lookupTable8[i] = (byte)Math.Clamp((int)(corrected * 255.0), 0, 255);
+            lut[i] = (byte)Math.Clamp((int)(corrected * 255.0), 0, 255);
         }
+        return lut;
     }
 
-    private void BuildLUT16(double gamma, double gain)
+    private static ushort[] BuildLUT16(double gamma, double gain)
     {
+        var lut = new ushort[65536];
         double invGamma = 1.0 / gamma;
         for (int i = 0; i < 65536; i++)
         {
             double normalized = i / 65535.0;
             double corrected = Math.Pow(normalized, invGamma) * gain;
-            _lookupTable16[i] = (ushort)Math.Clamp((int)(corrected * 65535.0), 0, 65535);
+            lut[i] = (ushort)Math.Clamp((int)(corrected * 65535.0), 0, 65535);
         }
+        return lut;
     }
 }

XP.ImageProcessing.Processors/图像增强/SuperResolutionProcessor.cs

@@ -145,7 +145,8 @@ public class SuperResolutionProcessor : ImageProcessorBase<byte>
         int w = inputImage.Width;
 
         // 获取模型输入信息
-        string inputName = session.InputMetadata.Keys.First();
+        string inputName = session.InputMetadata.Keys.FirstOrDefault()
+            ?? throw new InvalidOperationException("ONNX model has no input metadata");
         var inputMeta = session.InputMetadata[inputName];
         int[] dims = inputMeta.Dimensions;
         // dims 格式: [1, H, W, C] (NHWC)，C 可能是 1 或 3

XP.ImageProcessing.Processors/滤波处理/BandPassFilterProcessor.cs

@@ -85,7 +85,7 @@ public class BandPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
         if (highCutoff <= lowCutoff) highCutoff = lowCutoff + 10;
 
         var floatImage = inputImage.Convert<Gray, float>();
-        var imaginaryImage = new Image<Gray, float>(floatImage.Size);
+        using var imaginaryImage = new Image<Gray, float>(floatImage.Size);
         imaginaryImage.SetZero();
 
         using (var planes = new Emgu.CV.Util.VectorOfMat())
@@ -139,6 +139,8 @@ public class BandPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
                             result = (result - minVal) * (255.0 / (maxVal - minVal));
                         }
 
+                        floatImage.Dispose();
+                        mask.Dispose();
                         complexMat.Dispose();
                         dftMat.Dispose();
                         filteredDft.Dispose();

XP.ImageProcessing.Processors/滤波处理/HighPassFilterProcessor.cs

@@ -126,7 +126,7 @@ public class HighPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
     /// </summary>
     private Mat CreateHighPassFilter(int rows, int cols, double d0)
     {
-        var filter = new Image<Gray, float>(cols, rows);
+        using var filter = new Image<Gray, float>(cols, rows);
 
         int centerX = cols / 2;
         int centerY = rows / 2;
@@ -141,6 +141,6 @@ public class HighPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
             }
         }
 
-        return filter.Mat;
+        return filter.Mat.Clone();
     }
 }

XP.ImageProcessing.Processors/滤波处理/LowPassFilterProcessor.cs

@@ -122,7 +122,7 @@ public class LowPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
     /// </summary>
     private Mat CreateLowPassFilter(int rows, int cols, double d0)
     {
-        var filter = new Image<Gray, float>(cols, rows);
+        using var filter = new Image<Gray, float>(cols, rows);
 
         int centerX = cols / 2;
         int centerY = rows / 2;
@@ -137,6 +137,6 @@ public class LowPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
             }
         }
 
-        return filter.Mat;
+        return filter.Mat.Clone();
     }
 }

XP.ImageProcessing.Processors/滤波处理/RemoveOutliersProcessor.cs

@@ -106,6 +106,8 @@ public class RemoveOutliersProcessor<TDepth> : ImageProcessorBase<TDepth>
         // 逐像素比较并替换离群点
         var result = inputImage.Clone();
 
+        try
+        {
             for (int y = 0; y < height; y++)
             {
                 for (int x = 0; x < width; x++)
@@ -136,8 +138,11 @@ public class RemoveOutliersProcessor<TDepth> : ImageProcessorBase<TDepth>
                     }
                 }
             }
-
+        }
+        finally
+        {
             medianImage.Dispose();
+        }
 
         _logger.Debug("Process: KernelSize={K}, Threshold={T}, Type={Type}",
             kernelSize, threshold, outlierType);