Efficient Self-Calibrated Convolution For Real-Time Image Super-Resolution

There is growing interest in employing deep neural networks (DNN) for image sharpening. However, sharpening medical computed tomography (CT) images is challenging because sharpening substantially amplifies high-frequency noise. Alternatively, sharpening algorithms that are also designed to denoise produce images lacking texture. Most importantly, radiologists strongly prefer reading images at a consistent level of noise. Hence it is preferable that a sharpening algorithm not substantially change the noise energy or texture. in this work, we propose a noise preserving sharpening filter (NPSF) to sharpen CT images while keeping the noise energy and texture in the result similar to that of the input. We achieve this by adding appropriately scaled noise while training. Furthermore, the NPSF is characterized by three user-adjustable parameters which give flexibility to achieve a desired level of sharpness and noise. Our experiments show that the NPSF can sharpen noisy images while producing desired noise level and texture.