Deep residual network for color filter array image denoising

A method for training a deep residual network for image denoising, the method comprising:

• inputting a set of training image pairs, in which a training image pair comprising a noise-free image and a noisy image corresponding to the noise-free image and both the noise-free image and the noisy image have a mosaic pattern, into a deep residual network, the deep residual network comprising a first block, one or more residual blocks, and a final block, the first block comprises a mosaic stride convolutional layer;
• generating, using the deep residual network, a denoised image for each noisy image, with steps comprising:
• generating, using the mosaic stride convolutional layer, a convoluted output image from a noisy image, the mosaic stride convolutional layer comprises multiple strided convolutional channels to sample the noisy image into multiple sampled images with each sampled image filtered by a convolution at a stride into a filtered image, the filtered images from the multiple strided convolutional channels are merged into the convoluted output image;
• generating, using one or more residual blocks in the deep residual network, an intermediate output image from the convoluted output image;
• transforming, using a convolutional layer in the deep residual network, the intermediate output image to an estimated noise image;
• and
• subtracting the estimated noise image from the noisy image to obtain the denoised image;
• and
• training the deep residual network using a loss function involving a loss between a plurality of ground-truth noise-free raw images and corresponding denoised images.

A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by one or more processors, causes the steps for denoising a noisy image to be performed comprising:

• sampling the noisy image, in a mosaic stride convolutional layer comprising multiple strided convolutional filters, into multiple sampled images;
• filtering each of the multiple sampled images with a corresponding strided convolution filter with a convolution kernel at a stride into a filtered image;
• merging all filtered images into a convoluted output image;
• generating, using one or more residual blocks in the deep residual network, an intermediate output image from the convoluted output image, each residual block comprises one or more layers of convolutions and rectified linear units (ReLUs) with a shortcut connection spanning the one or more layers of convolutions and PReLUs;
• transforming, using a convolutional layer, the intermediate output image to an estimated noise image;
• and
• subtracting the estimated noise image from the noisy image to obtain a denoised image.