The deconvolution, or cleaning, of radio interferometric images often involves computing model visibilities from a list of clean components, in order that the contribution from the model can be subtracted from the observed visibilities. This step is normally performed using a forward fast Fourier transform (FFT), followed by a ‘degridding’ step that interpolates over the uv plane to construct the model visibilities. An alternative approach is to calculate the model visibilities directly by summing over all the members of the clean component list, which is a more accurate method that can also be much slower. However, if the clean components are used to construct a model image on the surface of the celestial sphere then the model visibilities can be generated directly from the wavelet coefficients, and the sparsity of the model means that most of these coefficients are zero, and can be ignored. We have constructed a prototype imager that uses a spherical-wavelet representation of the model image to generate model visibilities during each major cycle, and find empirically that the execution time scales with the wavelet resolution level, $J$, as $\mathcal{O}(1.07^J)$, and with the number of distinct clean components, $N_{\mathrm{C}}$, as $\mathcal{O}\left(N_{\mathrm{C}}\right)$. The prototype organises the wavelet coefficients into a tree structure, and does not store or process the zero wavelet coefficients.

无线电干涉仪图像的反卷积或清理通常涉及从干净组件列表中计算模型可见性，以便可以从观察到的可见性中减去模型的贡献。通常使用前向快速傅立叶变换（FFT）执行此步骤，然后执行在uv平面内插以构建模型可见性的“去网格化”步骤。另一种方法是直接对干净组件列表的所有成员求和，以直接计算模型可见性，这是​​一种更准确的方法，但也可能慢得多。但是，如果使用干净的分量在天球表面上构建模型图像，则可以直接从小波系数生成模型可见性，模型的稀疏性意味着这些系数大部分为零，可以忽略。我们构建了一个原型成像器，该模型成像器使用模型图像的球面小波表示形式在每个主要周期内生成模型可见性，并凭经验发现执行时间与小波分辨率水平成正比，$Ĵ$，作为 $\mathcal{Ø}（\mathrm{1个}。07^{Ĵ}）$，以及独特的清洁组件数量， ${ñ}_{\mathrm{C}}$，作为 $\mathcal{Ø}（{ñ}_{\mathrm{C}}）$。该原型将小波系数组织为树形结构，并且不存储或处理零小波系数。