Each module could be either parallel module or fusion module, which is determined by optimizing the architecture parametersαp and αf. Specifically,the learned twoarchitectures both contain eight fusion modules and four parallel modules, and the only one difference between them is the position ofthefusion andtheparallel modules. From theobservations, wecould conclude that: 1) themulti-scale information isremarkably important toimage restoration. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. From the top to the bottom for each image, the noise levels areσ = 30,50,70. From the left to the right are Input, BM3D[1],RED[9],WNNM[3],NLRN[6],DuRN-P [7],N3Net[10],CLEARER, andGround truth.

Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration.

In the paper, we present a multi-scale search space which is casted into a differentiable supernet consisting of three modules, i.e., parallel module, transition module, and fusion module. As shown in Figure 1.(a), there are As mentioned in the main body of the paper, the super-network we build for restoration contains three cells and each cell consists of four cascade modules. Namely, there are 12 cascade modules in total. The strided convolution is used to down sample features. The convolutional sequence is arranged in a residual manner for each parallel direction.

Weaknesses: 1: Limited novelty: CLEARER uses multi-scale search space that consists of three types of modules: parallel module, transition module, and fusion module. All of these modules were originally proposed in [2, 1].The authors did not cite these works when mentioning the said modules throughout the paper. It seems inconvenient, as for every new task we would have a different architecture. However, they did not provide any analysis/insights of what makes it specific for image restoration. For instance, what makes it suitable for image denoising and image deraining, OR why it would not work for any other applications such as semantic segmentation?

The paper initially received mixed ratings, but the reviewers were convinced by the rebuttal. Overall, their assessment is positive; the area chair agrees with their assessment and recommends an accept.

Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration. On one hand, we design a multi-scale search space that consists of three task-flexible modules. Namely, 1) Parallel module that connects multi-resolution neural blocks in parallel, while preserving the channels and spatial-resolution in each neural block, 2) Transition module remains the existing multi-resolution features while extending them to a lower resolution, 3) Fusion module integrates multi-resolution features by passing the features of the parallel neural blocks to the current neural blocks. On the other hand, we present novel losses which could 1) balance the tradeoff between the model complexity and performance, which is highly expected to image restoration; and 2) relax the discrete architecture parameters into a continuous distribution which approximates to either 0 or 1. As a result, a differentiable strategy could be employed to search when to fuse or extract multi-resolution features, while the discretization issue faced by the gradient-based NAS could be alleviated.

Before the start of India's general election in April, a top candidate looking to unseat Prime Minister Narendra Modi was not out wooing voters on the campaign trail. Arvind Kejriwal, the chief minister of Delhi and the head of a political party known for its anti-corruption platform, was arrested in late March for, yes, alleged corruption. His supporters hit the streets in protest, decrying the arrest as a politically motivated move by Modi aimed at weakening a rival. Soon after the arrest, Kejriwal implored his supporters to stay strong. "There are some forces who are trying to weaken our country and its democracy," he said in a 34-second audio clip posted to social media by a fellow party member.

Videos have become omnipresent, from streaming our favorite movies and TV shows to participating in video conferences and calls. With the increasing use of smartphones and other capture devices, the quality of videos has risen in importance. However, due to various factors like low light, digital noise, or simply low acquisition quality, the quality of videos captured by these devices is often far from perfect. In these situations, video enhancement techniques come into play, aiming to improve resolution and visual features. Over the years, various video enhancement techniques have been developed until the arrival of complex machine learning algorithms to remove noise and improve image quality.