There have been many image denoisers using deep neural networks, which outperform conventional model-based methods by large margins. Recently, self-supervised methods have attracted attention because constructing a large real noise dataset for supervised training is an enormous burden. The most representative self-supervised denoisers are based on blind-spot networks, which exclude the receptive field’s center pixel. However, excluding any input pixel is abandoning some information, especially when the input pixel at the corresponding output position is excluded. In addition, a standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise, though it successfully removes independently distributed synthetic noise. Hence, to realize a more practical denoiser, we propose a novel self-supervised training framework that can remove real noise. For this, we derive the theoretic upper bound of a supervised loss where the network is guided by the downsampled blinded output. Also, we design a conditional blind-spot network (C-BSN), which selectively controls the blindness of the network to use the center pixel information. Furthermore, we exploit a random subsampler to decorrelate noise spatially, making the C-BSN free of visual artifacts that were often seen in downsample-based methods. Extensive experiments show that the proposed C-BSN achieves state-of-the-art performance on real-world datasets as a self-supervised denoiser and shows qualitatively pleasing results without any post-processing or refinement.

Recent learning-based lossless image compression methods encode an image in the unit of subimages and achieve comparable performances to conventional non-learning algorithms. However, these methods do not consider the performance drop in the high-frequency region, giving equal consideration to the low and high-frequency areas. In this paper, we propose a new lossless image compression method that proceeds the encoding in a coarse-to-fine manner to separate and process low and high-frequency regions differently. We initially compress the low-frequency components and then use them as additional input for encoding the remaining high-frequency region. The low-frequency components act as a strong prior in this case, which leads to improved estimation in the high-frequency area. In addition, we design the frequency decomposition process to be adptive to color channel, spatial location, and image characteristics. As a result, our method derives an image-specific optimal ratio of low/high-frequency components. Experiments show that the proposed method achieves state-of-the-art performance for benchmark high-resolution datasets.

Multi-exposure high dynamic range (HDR) imaging aims to generate an HDR image from multiple differently exposed low dynamic range (LDR) images. It is a challenging task due to two major problems: (1) there are usually misalignments among the input LDR images, and (2) LDR images often have incomplete information due to under-/over-exposure. In this paper, we propose a disentangled feature-guided HDR network (DFGNet) to alleviate the above-stated problems. Specifically, we first extract and disentangle exposure features and spatial features of input LDR images. Then, we process these features through the proposed DFG modules, which produce a high-quality HDR image. Experiments show that the proposed DFGNet achieves outstanding performance on a benchmark dataset. Our code and more results are available at https://github.com/KeuntekLee/DFGNet.

This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is that we jointly estimate the pixel values and coding contexts from neighboring pixels by training a simple multilayer perceptron in a residual and channel-wise progressive manner. Specifically, we obtain accurate pixel prediction along with coding contexts that reflect the magnitude of local activity very well. These results are sent to an adaptive arithmetic coder that appropriately encodes the prediction error according to the corresponding coding context. Experimental results demonstrate the effectiveness of the proposed method in high-resolution datasets.

This letter presents a convolutional neural network (CNN) for image denoising, especially for the reduction of real noises. As a network topology, we adopt the dual path network (DPN) that combines the advantages of residual and densely connected networks. Using the DPN as a basic building block, we design a network that connects the DPN in dual path again with an attention mechanism. For efficient denoising of real noise images, we build a training set where noisy images are obtained from a heteroscedastic Gaussian noise model and in-camera pipeline. In addition, we augment the synthetic training set with a relatively small number of real noise data. In the experiments, the proposed method is shown to provide state-of-the-art performance in reducing both synthetic and real noises.