Image Deconvolution

The image deconvolution method discussed in this context originates from the research proposed by D. Krishnan and R. Fergus. Their approach involves reversing the convolution process to restore image details and sharpness. This technique is widely applied in computer vision and image processing for tasks such as image restoration, deblurring, and enhancement. From an implementation perspective, the method typically involves solving an inverse problem using optimization algorithms like Richardson-Lucy deconvolution or Wiener filtering. Key computational steps include estimating the point spread function (PSF), applying Fourier transforms for efficient convolution operations, and incorporating regularization terms to handle noise amplification. By leveraging image deconvolution techniques, we can achieve better understanding and analysis of image characteristics, extracting more valuable information from degraded or blurred images. The implementation often requires careful parameter tuning for the PSF model and regularization strength to balance detail recovery against noise suppression.