Recent deep learning-based image denoising methods have shown impressive performance; however, many lack the flexibility to adjust the denoising strength based on the noise levels, camera settings, and user preferences. In this paper, we introduce a new controllable denoising framework that adaptively removes noise from images by utilizing information from camera parameters. Specifically, we focus on ISO, shutter speed, and F-number, which are closely related to noise levels. We convert these selected parameters into a vector to control and enhance the performance of the denoising network. Experimental results show that our method seamlessly adds controllability to standard denoising neural networks and improves their performance. Code is available at https://github.com/OBAKSA/CPADNet.

Many self-supervised representation learning methods have achieved high performance in image classification tasks. However, these methods have limited performance on localization tasks such as object detection or semantic segmentation. Most self-supervised representation learning methods are optimized with only one global representation, which does not pay much attention to the spatial information in an image. We propose a simple and effective method that uses the positional relationships between the entities in an image by shuffling the convolution kernels. Our method extends current self-supervised learning and calculates the pixel-wise (dis) similarities between the output of the standard convolution kernels and that of the randomly shuffled convolution kernels. Our proposed method achieves higher performance on object detection, instance segmentation, and semantic segmentation when attached to recent self-supervised learning methods.